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仿射运动模型下的图像盲超分辨率重建算法

, PP. 648-655

Keywords: 仿射变换,盲超分辨率(BSR),运动估计,模糊辨识,规整化

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Abstract:

研究利用帧间存在仿射运动的低分辨率图像序列重建出更高光学分辨率图像的盲超分辨率(BSR)问题。首先给出一种基于特征向量的模糊核零空间矩阵构造方法。将模糊的零子空间约束作为一项规整化泛函,提出一种非参数化模糊辨识、运动估计和图像重建三重耦合问题的联合迭代算法。该算法采用一个二层优化策略:先将三重耦合的BSR问题分解为关于模糊的二次型和关于运动参数与图像的非线性最小二乘(NLS)问题,再采用Gauss-Newton方法求解该NLS问题。仿真实验结果表明,文中提出的仿射变换下的BSR算法能对图像空间移变退化过程进行更为精确的建模,比纯平移BSR算法有更强的局部纹理恢复能力。最后通过真实车牌图像序列展示该算法的适用性。

References

[1]  Irani M,Peleg S.Motion Analysis for Image Enhancement: Resolution,Occlusion,and Transparency.Journal of Visual Communication and Image Representation,1993,4(4): 324-335
[2]  Hardie R C,Barnard K J,Armstrong E E.Joint Map Registration and High-Resolution Image Estimation Using a Sequence of Undersampled images.IEEE Trans on Image Processing,1997,6(12): 1621-1633
[3]  Woods N A,Galatsanos N P,Katsaggelos A K.Stochastic Methods for Joint Registration,Restoration,and Interpolation of Multiple Undersampled Images.IEEE Trans on Image Processing,2006,15(1): 201-213
[4]  Yap K H,He Y,Tian Y,et al.A Nonlinear L1-Norm Approach for Joint Image Registration and Super-Resolution.IEEE Signal Processing Letters,2009,16(11): 981-984
[5]  Nguyen N,Milanfar P,Golub G.Efficient Generalized Cross-Validation with Applications to Parametric Image Restoration and Resolution Enhancement.IEEE Trans on Image Processing,2001,10(9): 1299-1308
[6]  Yang Hao,Gao Jianpo,Wu Zhenyang.Blur Identification and Image Super-Resolution Reconstruction Using an Approach Similar to Variable Projection.IEEE Signal Processing Letters,2008,15(3): 289-292
[7]  He Yu,Yap K H,Chen Li,et al.Blind Super-Resolution Image Reconstruction Using a Maximum a Posteriori Estimation // Proc of the IEEE Conference on Image Processing.Atlanta,USA,2006: 1729-1732
[8]  Xiong Zhiwei,Sun Xiaoyan,Wu Feng.Robust web Image Video Super-Resolution.IEEE Trans on Image Processing,2010,19(8): 2017-2028
[9]  Yang Jianchao,Wright J,Huang T S,et al.Image Super-Resolution via Sparse Representation.IEEE Trans on Image Processing,2010,19(11): 2861-2873
[10]  Gajjar P P,Joshi M V.New Learning Based Super-Resolution: Use of DWT and IGMRF Prior.IEEE Trans on Image Processing,2010,19(5): 1201-1213
[11]  He Hu,Kondi L P.A Regularization Framework for Joint Blur Estimation and Super-Resolution of Video Sequences // Proc of the IEEE Conference on Image Processing.Genoa,Italy,2005: 329-332
[12]  Omer O A,Tanaka T.Joint Blur Identification and High-Resolution Image Estimation Based on Weighted Mixed-Norm with Outlier Rejection // Proc of the IEEE International Conference on Acoustics,Speech and Signal Processing.Las Vegas,USA,2008: 1305-1308
[13]  Chen Yuanxu,Luo Yupin,Hu Dongcheng.Unified Regularization Framework for Blind Image Super-Resolution.Optical Engineering,2007,46(12): 127001
[14]  Sroubek F,Cristobal G,Flusser J.A Unified Approach to Superresolution and Multichannel Blind Deconvolution.IEEE Trans on Image Processing,2007,16(9): 2322-2332
[15]  Zhang Xuesong.Algorithms for Super-Resolution Reconstruction from Low Quality Facial Images.Ph.D Dissertation.Beijing,China: Chinese Academy of Sciences.Institute of Automation,2009 (in Chinese)(张雪松.低质量人脸图像超分辨率重建算法研究.博士学位论文.北京:中国科学院自动化研究所,2009)
[16]  Zhang Xuesong,Jiang Jing,Peng Silong.Eigen-Subspace Regularized Face Image Super-Resolution Reconstruction.Journal of Computer-Aided Design Computer Graphics,2010,22(3): 487-493 (in Chinese)(张雪松,江 静,彭思龙.特征子空间规整化的人脸图像超分辨率重建.计算机辅助设计与图形学报,2010,22(3): 487-493)
[17]  Babacan S D,Molina R,Katsaggelos A K.Variational Bayesian Super Resolution.IEEE Trans on Image Processing,2011,20(4): 984-999

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